In the early 2000s, traditional computer vision methods were proposed for curb detection. In [1]}, the authors proposed a histogram filter with a threshold to locate curb points. Whereas, in [2]}, a Kalman filter based tracking of the threshold-based detected curb points was proposed. By the mid-2000s, Hough transform based method [3]} on 2D lidar data was explored under the assumption that the terrain was flat. Polyfit on a digital elevation map (DEM) created from the stereo vision was explored in [4]}, [5]}, while the authors in [6]} used height images from lidars to create DEM and applied polynomial fitting. In [7]} the authors proposed to create a DEM from ego-motion and filtering for curb points. As 3D lidar was adopted by researchers after the mid-2000s, a lot of the work for curb detection was based on ground plane segmentation on 3D data [8]}, [9]}, [10]}.
